Diversity-enhanced Genetic Algorithms for Dynamic Optimization

Yesterday, in Lisbon, I defended my PhD thesis on Evolutionary Algorithms for dynamic optimization.  The pdf of the document is here, and this is the abstract:

Many industrial applications have dynamic components that lead to variations of the fitness function and Genetic Algorithms (GAs) adaptiveness is an appropriate tool to solve this type of problems. The thesis proposes two new evolutionary methods to tackle dynamic problems. The first acts upon mating and avoids crossover between similar individuals, via a self-regulated mechanism, thus preserving genetic diversity. The second is a new mutation operator able to evolve self-regulated mutation rates with a particular distribution that is suited for dynamic optimization. Finally, an efficient hybrid method that combines both strategies is proposed. The objective and main claim is the possibility of designing nature-inspired protocols for GAs that are efficient when evolving on dynamic environments while preserving algorithms’ complexity and not requiring a priori information about the problem.

The proposals are tested on a wide range of problems and are able to outperform frequently other GAs, namely when the frequency of change is lower. The hybrid scheme proves to be particularly effective since it broadened the range of dynamics in which each method by itself excels. As projected, the proposed techniques are robust and do not increase parameters’ set, thus fulfilling necessary conditions for real-world applications.

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Carlos M. Fernandes was born in Luanda in 1973 and lives in between Lisbon, Portugal, and Granada, Spain. He graduated (Technical University of Lisbon, 1998) in Electrotechnics Engineering and owns a master degree in the same field since 2002 (Technical University of Lisbon). He is currently pursuing a Ph.d. on Bio-inspired Computing. From 2001 to 2005 he was an assistant at Instituto Politécnico de Setúbal. (He is also a photographer and photography teacher.) Bio-inspired Computing is his major field of research: Genetic Algorithms, Estimation of Distribution Algorithms, Ant Colony Optimization, Particle Swarm Optimization and other metaheuristics. He is particularly interested in the hybridization of Bio-inspired Computing techniques with Self-Organization, Self-Organized Criticality Models and diversity maintenance strategies. In the present, Dynamic Optimization Problems are his mains target for applying such techniques. website: www.carlosmfernandes.com email: c.m.fernandes.photo@gmail.com

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